Basic Stucture

The Basis for this course is to develop an understanding of statistical inference. This class will focus on a wide variety of topics; however the general principle is that of making informed decisions from data. This course will focus on various methods of parameter estimation, hypothesis testing, and interval estimation. Topics in decision theory will also be discussed. Both theoretical and computational methods will be examined in this class.

It is the mark of a truly intelligent person to be moved by statistics. -George Bernard Shaw

Topics to be discussed in this course follow as:

• Inferential methods for parameter estimation.
• Order statistics and their distributions.
• Approximate methods: The Delta Method, and computational methods
• Convergence theorems and their use in actual statistical practice.

• Principles of inference: Likelihood, Sufficiency, and Conditionality.

• Both Classical and Bayesian treatments of inference and hypothesis testing will be examined.
• Asymptotic and Computational methods will be compared and discussed

Many examples will be centered around the above. You will get a lot of practice comparing theoretical results to practical and simulated scenarios.

We will next move on to:

• Interval estimation.
• Confidence, coverage, and pivots.
• An introductory treatment of Bayesian statistics, and the commonly applied estimation algorithms (MCMC) will be discussed.